A new optimized Stockwell transform applied on synthetic and real non-stationary signals

Moukadem, Ali; Bouguila, Z.; Abdeslam, Djaffar Ould; Dieterlen, Alain

doi:10.1016/j.dsp.2015.07.003

Cited by 76 publications

(57 citation statements)

References 19 publications

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“…Select their values empirically may not be adequate for some types of signals. To provide automatic selection of these parameters with respect to the analyzed signal, the methodology proposed in [23,26] is adopted. The objective function is maximizing the energy concentration:…”

Section: Optimization Of the Scaling Parametermentioning

confidence: 99%

“…f min = 1, is assumed to be the first voice while f max depends on the analyzed signal. As in [26], K and L are respectively fixed to 10 and 1000. Developing the double inequality (19) leads to the constraints:…”

Section: Optimization Of the Scaling Parametermentioning

confidence: 99%

“…This later would then become heavier. The constrained optimization problem (21) is resolved using an activeset based algorithm [32] as used in [26]. This strategy aims to transform the problem into a basis easier one to be used in an iterative process.…”

Section: Optimization Of the Scaling Parametermentioning

confidence: 99%

“…In the paragraphs above, some existing versions of modified S-transform have been cited, notably that presented in [26] generalizing the standard ST and the versions introduced in [23][24][25]. In order to evaluate the performance of the CSK-ST compared with other versions of S-transform, the proposed approach is applied to the same synthetic signals used in [26] where a rigorous comparison with [23][24][25]…”

Section: Comparison With Other Versions Of S-transformmentioning

confidence: 99%

“…A parameter which maximizes the energy concentration is also selected in [23]. Recently, Moukadem et al [26] introduced a new version of S-transform where energy concentration enhancement is provided. It also generalizes the standard S-transform and the modified ones presented in [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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S-transform based on compact support kernel

Zidelmal¹,

Hamil²,

Moukadem³

et al. 2017

Digital Signal Processing

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In this paper, the use of a Compact Support Kernel (CSK) instead of the Gaussian window in the Stransform is proposed. The CSK is derived from the Gaussian but overcomes its practical drawbacks while preserving a large number of its useful properties. The width of the CSK is controlled by some parameters making it more flexible. These parameters are selected to optimize the energy concentration in the timefrequency domain. Compared to other versions of S-transform, other time-frequency representations and continuous wavelet transform, the achieved results obtained using synthetic and real data show a significant improvement in the time and frequency resolution, energy concentration and instantaneous frequency estimation.

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Section: Optimization Of the Scaling Parametermentioning

confidence: 99%

Section: Optimization Of the Scaling Parametermentioning

confidence: 99%

Section: Optimization Of the Scaling Parametermentioning

confidence: 99%

Section: Comparison With Other Versions Of S-transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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S-transform based on compact support kernel

Zidelmal¹,

Hamil²,

Moukadem³

et al. 2017

Digital Signal Processing

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Neuromuscular disease detection based on feature extraction from time–frequency images of EMG signals employing robust hyperbolic Stockwell transform

Samanta

Chatterjee

Bose

2022

Int J Imaging Syst Tech

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In this paper, a novel technique for detection of healthy (H), myopathy, (M) and amyotrophic lateral sclerosis (ALS) electromyography (EMG) signals is proposed employing robust hyperbolic Stockwell transform (HST). HST is an efficient signal processing technique to analyze any nonstationary signal in joint time-frequency (T-F) plane. However, a major issue with HST is the optimum selection of Gaussian window parameters since the resolution in the T-F plane depends on the shape of the window. Considering the aforesaid fact, in this article, a genetic algorithm (GA) based optimized HST is proposed for improved EMG signal analysis in T-F plane. Several novel features were extracted from HST spectrum and features with high statistical significance were selected for classification using several benchmark classifiers. It was observed that optimized HST resulted in better classification accuracy of EMG signals which indicates its potential for clinical applications.

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Short‐term wind speed forecasting using S‐transform with compactly supported kernel

Kamath

Senapati

2020

Wind Energy

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This paper presents a modified S‐transform (ST) based on a compactly supported kernel. A version of Cheriet‐Belochrani (CB) kernel is chosen for this purpose. It is shown that the proposed modified S‐transform (CBST) offers better frequency resolution than the traditional ST. It is used to decompose the wind speed time series into frequency‐based subseries. Further, artificial neural network (ANN) is applied to each of the subseries for an hour ahead prediction. Finally, forecast for the original wind speed series is obtained by combining the prediction result of all the subseries. Initially, increasing the number of subseries results in a decrease in prediction error. However, when the number of subseries is sufficiently large, no significant change in prediction error is observed if the number is further increased. It is also observed that, for a model based on neural‐network, involving decomposition of wind speed time series, the proposed model offers low prediction error. A comparative study with the methods based on wavelet transform (WT) and empirical mode decomposition (EMD) demonstrates the effectiveness of the proposed method. For this study, we have used simulated wind speed data generated by nonhydrostatic mesoscale model and data recorded using anemometer and LiDAR instrument at different heights to evaluate the short‐term forecasting results.

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A new optimized Stockwell transform applied on synthetic and real non-stationary signals

Cited by 76 publications

References 19 publications

S-transform based on compact support kernel

S-transform based on compact support kernel

Neuromuscular disease detection based on feature extraction from time–frequency images of EMG signals employing robust hyperbolic Stockwell transform

Short‐term wind speed forecasting using S‐transform with compactly supported kernel

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